Method for using environmental classification to assist in financial management and services

ABSTRACT

Managing risks of crop production can be performed by understanding the relative performance of different agricultural inputs under the same or similar environmental conditions. In addition, managing of crop production risks can be performed by understanding variations in the performance of the same agricultural inputs over a range of environmental conditions. By being able to describe and understand these variations in performance, decisions can be made which are consistent with overall business and/or production objectives and limit risk associated with variations in environmental conditions. In addition to producers there are other stakeholders in the crop production process, such as financial institutions, insurance providers, users of crops produced, and input suppliers. These and other stakeholders can provide financial incentives to producers for managing crop production risks through use of environmental classification and/or genotype-by-environment information.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 119 of a provisionalapplication Ser. No. 60/689,716 filed Jun. 10, 2005, which applicationis hereby incorporated by reference in its entirety.

BACKGROUND OF THE INVENTION

The present invention provides for computer-implemented methods andrelated methods which tie financial management and/or financial servicesto the use of environmental classification in making agriculturalproduction decisions.

Agricultural production has attendant uncertainties and risks. Managingsuch risks are important not only to the success of the productionoperation, but also to related industries. These include, withoutlimitation, banks or other financial institution that provide financingfor producers; input suppliers, such as, but not limited to seedsuppliers, chemical suppliers, equipment suppliers, and others;purchasers or users of the produced crops, including livestockproducers, ethanol or bio-diesel producers, and food manufacturers.Thus, there are many potential stakeholders in agricultural production.

What is needed is a method for product selection that is useful incharacterizing relative performance of different agricultural inputsunder different conditions so that risk can be managed in a way that canassist in making financial management decisions, including not onlydecisions made by producers, but also decisions made by otherstakeholders.

SUMMARY OF THE INVENTION

Therefore it is a primary object, feature, or advantage of the presentinvention to improve over the state of the art.

Another object, feature, or advantage of the present invention is toprovide a method to assist stakeholders in agricultural production inmanaging financial risks associated with crop production.

Yet another object, feature, or advantage of the present invention is toassist stakeholders in agricultural production and others inunderstanding relative performance of different agricultural inputs,including seed products, under the same or similar environmentalconditions.

A still further object, feature, or advantage of the present inventionis to assist stakeholders in agricultural production and others inunderstanding relative performance of an agricultural input, such as aseed product, under a range of environmental conditions.

Another objective, feature, or advantage of the present invention is toassist producers in selecting the best seed product for a particularlocation.

Yet another object, feature, or advantage of the present invention is toprovide additional incentives to producers for selecting seeds productsfor a particular location with a greater likelihood of desiredperformance.

According to one aspect of the present invention a method for reducingrisk associated with making a financial decision related to cropproduction is provided. The method includes identifying a land base forthe crop production, classifying the land base to provide anenvironmental classification, receiving an indication of the seedproduct selected for production, and evaluating relative risk associatedwith the production of different genotypes of seed products by comparingpredicted relative performance of a plurality of seed products, eachseed product having a genotype. The predicted relative performance is atleast partially based on predicted genotype by environment interactionsbetween each seed product and the environmental classification of theland base. The plurality of seed products includes the seed productselected for production. The method further includes providing afinancial decision at least partially based on the risk associated withthe use the seed product selected for production relative to one or moreother seed products within the plurality of seed products. Theenvironmental classification may be based on data collected from aplurality of locations over a number of years to provide informationregarding the frequency of particular environmental classifications atvarious locations. Thus, experience with a genotype over a variety ofdifferent environments assists in determining and applying theenvironmental classifications.

The financial decision may of various types or kinds. For example, thefinancial decision may be a determination of whether to finance the cropproduction, whether to contract for purchasing crops resulting from thecrop production, or a determination of terms of financing for the cropproduction. The performance may be measured in various ways, includingyield, content (such as, but not limited to protein content, oilcontent, starch content, moisture content), or quality. The performancemay be related to the end use of the crop, such as use in ethanolproduction, bio-diesel production, livestock use, or food manufacturing.

According to another aspect of the present invention, a method forproviding financing is provided. The method includes evaluating the useof agricultural inputs associated with a producer according to anenvironmental classification system. Each of the agricultural inputs areclassified according to the environmental classification system. A landbase associated with the producer is also classified according to theenvironmental classification system. A financing decision associatedwith the producer is made based on the results form the step ofevaluating. The financing decision may of numerous types. For example,without limitation, the financing decision can be a decision as towhether or not to finance the producer or a decision regarding the termsof financing. These decisions are preferably related to an assessment ofthe production risks using the environmental classification system.

According to another aspect of the present invention, a method forproviding a financial incentive for use of an environmentalclassification system in making production management decisions isprovided. The method includes providing to a producer recommendations ofagricultural inputs to use. The recommendations are based, at least inpart, on environmental classification associated with the agriculturalinputs and an environmental classification associated with a land baseof the producer. The method further provides for giving the producer afinancial incentive to select agricultural inputs based on therecommendations. The financial incentives may include a reduced purchaseprice for one or more of the agricultural inputs, preferred financingterms, a reduced interest rate on financing, or other types of financialincentives.

According to another aspect of the present invention, a method forproviding a financial incentive for use of genotype by environmentinformation in selecting a seed product is provided. The method includesproviding to a producer a recommendation of one or more seed products touse to produce crop on a land base of the producer. The recommendationbased in whole or in part on relative performance of seed products underenvironmental conditions associated with the land base of the producerand interactions between the genotype of each of the plurality of seedproducts and the environmental conditions. The method further includesgiving the producer a financial incentive to accept the recommendation.The financial incentive may be of various kinds or types. The financialincentive may include a reduced purchase price for one or more of theagricultural inputs, preferred financing terms, a reduced interest rateon financing, and/or other types of financial incentives.

According to another aspect of the present invention, a method forproviding a crop and/or revenue insurance policy to a producer isprovided. The method includes receiving an evaluation of the use ofagricultural inputs associated with a producer according to anenvironmental classification system wherein each of the agriculturalinputs being classified according to the environmental classificationsystem and a land base associated with the producer being classifiedaccording to the environmental classification system. The method furtherincludes determining one or more terms of the crop insurance policy atleast partially based on the step of evaluating use of agriculturalinputs and providing the crop insurance policy to the producer. Theenvironmental classification system is preferably at least partiallybased on genotype by environment interactions. The terms of the crop orrevenue insurance policy may take into account the risk associated withparticular product decisions or with the use of particular agriculturalinputs. The terms of the crop or revenue insurance may be based on thehistory of a producer or decisions for the upcoming or current crop.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a flow diagram illustrating one process for determininggenotype-by-environment interactions and using that information incategorizing land bases into different environmental classifications.

FIG. 2A to FIG. 2C provide an example of genotype by environmentinteractions and cross-over interactions between two different varietiesin four different environmental classes.

FIG. 3 illustrates environment-standardized GGE biplot of grain yield of18 maize hybrids (H1-H18) grown in 266 environments over three yearsstratified by state.

FIG. 4 illustrates environment-standardized GGE biplot of grain yield of18 maize hybrids (H1-H18) grown in 266 environments over three yearsstratified by environmental class.

FIG. 5 illustrates one possible schematic for categorizing differentland bases into environmental classifications based on temperatures,solar radiation, and length of photoperiod.

FIG. 6 is a bar graph representation of the frequency of variousenvironmental classes among target population of environments (TPEs) ormulti-environment trials (METs).

FIG. 7 illustrates potential categories of environmental classesidentified throughout the United States in 1988 and their locations;these include temperate, temperate dry, temperate humid, high latitude,and subtropical classes.

FIG. 8 is a flow diagram illustrating information flow from anenvironmental profile and a producer profile to providingrecommendations to a producer according to one embodiment of the presentinvention.

FIG. 9 is block diagram illustrating a system for determining productrecommendations according to one embodiment of the present invention.

FIG. 10 is a screen display according to one embodiment of the presentinvention.

FIG. 11 is a screen display showing a product portfolio according to oneembodiment of the present invention.

FIG. 12 is a screen display for one embodiment of an application of thepresent invention.

FIG. 13 is a screen display for one embodiment of an application of thepresent invention.

FIG. 14 is a screen display for one embodiment of the present inventionshowing field-by-field product recommendations.

FIG. 15 is a flow diagram for one embodiment of a sales tool fordemonstrating the value of environmental classification in describingrelative performance.

FIG. 16 is a screen display illustrating one example of output from asales tool of the present invention for demonstrating the value ofenvironmental classification in describing relative performance.

FIG. 17 is a flow diagram showing information flow in a productselection and positioning application of the present invention.

FIG. 18 is a block diagram illustrating financial and insuranceinteractions associated with agriculture production.

FIG. 19 is a block diagram illustrating one embodiment of a system ofthe present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The present invention provides methods which tie financial managementand/or financial services to the use of environmental classification inmaking production decisions. The present invention uses environmentalclassification and/or an understanding of genotype-by-environmentinteractions to manage production risks and recognizes that financialrisks can be better managed where the production risks are managed.Thus, financial decisions can be made, or financial incentives providedbased upon knowledge of genotype-by-environmental classifications and/orenvironmental classification.

Managing risks of crop production can be performed by understanding therelative performance of different agricultural inputs, includingdifferent seed products, under the same or similarly definedenvironmental conditions. In addition, managing of crop production riskscan be performed by understanding variations in the performance of thesame agricultural input over a range of environmental conditions. Bybeing able to describe and understand the reasons behind thesevariations in performance, decisions can be made which are consistentwith overall business and/or production objectives and limit riskassociated with variations in environmental conditions. These decisionscan include what seed products or combination of seed products to plant,where to plant different seed products, what other agricultural inputsto use, and what crop management practices to apply.

One method to manage risks associated with crop production usesknowledge of genotype-by-environment interactions to assist a produceror other customer in selecting seed products to plant in one or morefields. A “genotype” is generally defined as a cultivar, geneticallyhomogenous (lines, clones), a hybrid of two or more parents, orheterogeneous (open-pollinated populations). An “environment” isgenerally defined as a set of conditions, such as climatic conditions,soil conditions, biotic factors (such as, without limitation, pests anddiseases) and/or other conditions that impact genotype productivity.“Management” as used in this context generally refers to productionmanagement decisions, such as, but not limited to crop productionpractices. In addition, the present invention allows for the use ofenvironmental characterizations to assist in describinggenotype-by-environment interactions. It is to be understood that theterm “genotype-by-environment” (G×E) is to encompass what is sometimesknown or referred to as “genotype-by-environment-by management” (G×E×M)as the environment associated with a plant may include managementpractices which affect the environment (for example, irrigation may beconsidered a management practice, but use of irrigation affects thegrowing environment).

Following, is an exemplary description regarding the use of G×Einteractions and environmental classification. Next, an exemplarydescription is provided regarding how a producer or other stakeholderuses this information in order to make management decisions. Then, theways in which financial management and financial incentives can be tiedto knowledge of G×E interactions and/or environmental classification isdiscussed.

G×E and Environmental Classification

Genetic manipulation alone does not ensure that a plant will performwell in a specific environment or for that matter a wide range ofenvironments year after year. In other words, there is no one genotypethat is likely to perform best in all environments or under allmanagement practices. The performance or phenotype results from aninteraction between the plant's genotype and the environment and themanagement practices used.

It is to be understood that there are some inherent difficulties inunderstanding such interactions. An environment at a given locationchanges over the years making multi-environment trials (METs) performedin the same location limited as to inferences about future cropperformance. Furthermore, inferences about a crop's future performancein different locations depend on whether the target population ofenvironments (TPEs) is well sampled since the environment varies betweendifferent locations in one year.

To assist in analyzing such interactions, the present inventionpreferably uses environmental classification techniques. Theenvironmental classification techniques are used, preferably with alarge set of data to relate performance of different genotypes todifferent environments. Environmental classification is then used whenselecting the best seed products for a particular land base. Thus, forexample, a producer can use environmental classification to select thebest seed products for their land base based on the expectedenvironmental conditions. Alternatively, the producer may diversify andselect a combination of seed products based on a range of expectedenvironmental conditions to thereby manage risks associated withenvironmental variability. Of course, environmental classification canbe used by not just producers but others having interest in agriculturalproduction.

FIG. 1 provides an overview of one G×E paradigm where G×E knowledge 12is used in planning and positioning 18. G×E knowledge 12 can be appliedto crop modeling 14. Crop modeling 14 and G×E knowledge 12 may eitheralone or together be used to classify environments. The G×E knowledge 12and classified environments may be used in facilitating the positioningand/or planning 18 strategies, such as characterization of products,resource efficiency, risk management, product positions, and productselection.

Subsequent to positioning and planning, the producer will grow theselected products 26 and measure the performance results 24. Theproducer may also collect environmental and physiological landmark data28 and in conjunction with performance results 24 use it in analysis 20.Analysis of environmental and physiological landmark data 28 andperformance results 24 may undergo analysis 20 using G×E analysis toolsor period-of-years database 22. In addition, the output of records ofproduct use and production practices can assist in decision making andregulatory compliance.

Building an Environmental Classification System

The effectiveness of a product evaluation system for genotypeperformance largely depends on the genetic correlation betweenmulti-environment trials (MET) and the target population of environments(TPE) (Comstock, R. E. 1977. ‘Proceedings of the InternationalConference on Quantitative Genetics, Aug. 16-21, 1976’ pp. 705-18. IowaState University Press. Ames, USA.). For example, previouscharacterizations of maize environments relied mainly on climatic andsoil data (e.g. Hartkamp, A. D., J. W. White, A. Rodriguez Aguilar, M.Bänziger, G. Srinivasan, G. Granados, and J. Crossa. 2000. MaizeProduction Environments Revisited: A GIS-based Approach. Mexico, D. F.CIMMYT.; Pollak, L. M., and J. D. Corbett. 1993. Agron. J. 85:1133-1139;Runge, E. C. A. 1968. Agron. J. 60:503-507.). While useful to describeenvironmental variables affecting crop productivity, these efforts didnot quantify the impact of these variables on the genetic correlationsamong testing sites. Consequently, plant breeders have more extensivelyused characterizations of environments based on similarity of productdiscrimination in product evaluation trials (e.g. Cooper, M., D. E.Byth, and I. H. DeLacy. 1993. Field Crops Res. 35:63-74.). However,these efforts frequently fail to provide a long-term assessment of thetarget population of environments (TPE), mainly due to the cost andimpracticality of collecting empirical performance data for widespreadand long-term studies.

The present invention provides a modern approach of product evaluationwhere a TPE is described. The description of a TPE includes classifyingthe land base into an environmental class and assessing the frequency ofoccurrence of the range of environments experienced at a given location.The present inventors contemplate that areas of adaption (AOA) couldalso be evaluated. As used herein AOA refers to a location with theenvironmental conditions that would be well suited for a crop orspecific genotype. Area of adaption is based on a number of factors,including, but not limited to, days to maturity, insect resistance,disease resistance, and drought resistance. Area of adaptability doesnot indicate that the crop will grow in every location or every growingseason within the area of adaption or that it will not grow outside thearea. Rather it defines a generally higher probability of success for acrop or genotype within as opposed to outside that area of adaptation.

The environmental information collected may be used to develop anenvironmental database for research seed product locations, or growercommodity production locations. Initially, multiple environment trialsare performed by planting different genotypes available from a varietyof sources, e.g. germplasm, inbreds, hybrids, varieties in multipleenvironments. These trials aid the determination of whether the TPEs arehomogenous or should be categorized into different environmentalclassifications. The performance data of these genotypes andenvironmental and/or physiological landmark data from the MET arecollected and entered into a data set. For example, performance datacollected for a genotype of corn may include, but is not limited to, anyof the following: yield, grain moisture, relative maturity, stalklodging, stand establishment, emergence, midsilk, test weight, protein,oil, and starch. Yield refers to bushels of grain per acre. Grainmoisture refers to a moisture determination made from each plot atharvest time, using an instrument such as an electrical conductancemoisture meter. Stalk lodging refers to the determination of the numberof broken stalks in each plot prior to harvest. Stand establishmentrefers to the differences between the desired planting rate for eachhybrid and the final stand. Emergence refers to an emergence count madeon each plot after plant emergence where emergence percentage may becomputed based on the number of plants and the number of kernelsplanted. The mid silk date is the Julian day of the year in which 50% ofthe plants show silks at one site in a region. The test weights aretypically reported as pounds per bushel on grain samples at fieldmoisture. Protein, oil and starch are typically reported as a percentprotein, oil, and starch content at a designated percent grain moistureon dried samples using standard methods, for example, a near infraredtransmittance whole grain analyzer.

One skilled in the art would be familiar with performance data collectedfor other crops, for example, soybeans, wheat, sunflowers, canola, riceand cotton. Performance data for soybeans include, without limitation,relative maturity, soybean cyst nematode tolerance/resistance, plantheight, lodging score, seed size, protein and oil percentage,Phytophthora resistance genes, Phytophthora partial resistance,Sclerotinia rating, and yield. Relative maturity refers to adetermination that is designed to account for factors, such as soybeanvariety, planting date, weather, latitude and disease that affectmaturity date and number of days from planting to maturity. Plant heightrefers to a determination of the soybean plant's height, usuallydetermined prior to harvest. Lodging, traditionally, the verticalorientation of the plant, i.e. the degree to which the plant is erect.The lodging of a soybean plant is traditionally rated by researchersusing a scale of 1 to 9 as follows: 1.0=almost all plants erect,3.0=either all plants leaning slightly, or a few plants down, 5.0=eitherall plants leaning moderately (45 degree angle), or 25-50% down,7.0=either all plants leaning considerably, or 50-80% down, 9.0=allplants prostrate. The seed size of a soybean plant typically refers tothousands of seeds per pound. Protein and oil percentage analysis may bedetermined using near infrared transmittance technology and reported at13% moisture. Phytophthora resistance genes may be determined using ahypocotyl inoculation test with several races of Phytophthora todetermine the presence or absence of a particular Rps gene in a soybeanplant. Soybeans may also be evaluated for phytophthora partialresistance using a ratings system, where ratings of 3.0 to 3.9 areconsidered high levels of partial resistance, ratings of 4.0 to 5.9 areconsidered moderate, ratings over 6.0 indicate very little partialresistance or protection against Phytophthora. Soybeans may also beevaluated for partial resistance to Sclerotinia. Yield refers to bushelsper acre at 13 percent moisture.

Typical performance data for wheat includes, without limitation, testweight, protein percent, seed size, percent lodging, plant height,heading date, powdery mildew, leaf blotch complex (LBC), Fusarium headscab (FHS), flour yield, and flour softness. Test weight refers to adetermination of pounds/bushell using harvest grain moisture. Seed sizerefers to thousands of harvested seeds per pound. Percent lodging asdescribed previously refers to a rating system used to estimate thepercent of plants that are not erect or lean more than 45 degrees fromvertical. Plant height refers to the distance from the soil surface tothe top of the heads. Heading date refers to the average calendar day ofthe year on which 50 percent of the heads are completely emerged. Wheatinfected with powdery mildew (PM) may be determined using a scale systemwhere each plot is rated based on a 0 to 10 scale where: 0=0 to trace %leaf area covered; 1=leaf 4 with trace—50%; 2=leaf 3 with 1-5%; 3=leaf 3with 5-15%; 4=leaf 3 with>15%; 5=leaf 2 with 1-5%; 6=leaf 2 with 5-15%;7=leaf 2 with>15%; 8=leaf 1 with 1-5%; 9=leaf 1 with 5-15%; and 10=leaf1 with>15% leaf area covered (leaf 1=flag leaf). This scale takes intoaccount the percentage leaf area affected and the progress of thedisease upward on the plants. Leaf blotch complex (LBC) caused byStagonospora nodorum, Pyrenophora tritici-repentis and Bipolarissorokiniana for example may be determined when most varieties are in thesoft dough growth stage and rated based on the percentage of flag leafarea covered by leaf blotches. Fusarium head scab (FHS) caused byFusarium graminearum for example may be determined when plants are inthe late milk to soft dough growth stage and each plot is rated based ona disease severity estimate as the average percentage of spikeletsaffected per head. Flour yield refers to the percent flour yield frommilled whole grain. Flour softness refers to the percent offine-granular milled flour. Values higher than approximately 50 indicatekernel textures that are appropriate for soft wheat. Generally, highvalues are more desirable for milling and baking.

Typical performance data for sunflower includes, without limitation,resistance to aphids, neck breakage, brittle snap, stalk breakage,resistance to downy mildew (Plasmopara halstedii), height of the head atharvest, seed moisture, head shape, hullability, resistance to thesunflower midge, Contarinia schulzi, percentage of oil content, seedsize, yield, seedling vigor, and test weight. Resistance to aphidsrefers to a visual ratings system indicating resistance to aphids basedon a scale of 1-9 where higher scores indicate higher levels ofresistance. Neck breakage refers a visual ratings system indicating thelevel of neck breakage, typically on a scale from 1 to 9 where thehigher the score signifies that less breakage occurs. Brittle snaprefers to a visual rating system indicating the amount of brittle snap(stalk breakage) that typically occurs in the early season due to highwinds. The ratings system is based on a scale, usually ranging from 1-9,with a higher score denoting the occurrence of less breakage. Asunflower's resistance to Downy Mildew (Plasmopara halstedii) may bedetermined using a visual ratings scaled system with 9 being the highestand 1 the lowest. A higher score indicates greater resistance. Height ofthe head at harvest refers to the height of the head at harvest,measured in decimeters. Seed moisture refers to a determination of seedmoisture taken at harvest time, usually measured as a percentage ofmoisture to seed weight. Head shape of a sunflower is measured visuallyusing a scale system where each plot is rated based on a 1 to 9 scalewhere: 1=closed “midge” ball; 2=trumpet; 3=clam; 4=concave; 5=cone;6=reflex; 7=distorted; 8=convex; 9=flat. Hullability refers to theability of a hulling machine to remove seed hulls from the kernel,typically measured on a 1-9 scale where a higher score reflects betterhullability. Resistance to the sunflower midge, Contarinia schulzi, isdetermined based on head deformation which is rated on a 1-9 scalewhere: 9=no head deformation (fully resistant), 5=moderate headdeformation, 1=severe head deformation (fully susceptible). Thepercentage of oil content from the harvested grain is measured andadjusted to a 10% moisture level. The oil content of a sunflower seedmay be measured for various components, including palmitic acid, stearicacid, oleic acid, and linoleic acid, using a gas chromatograph. Seedsize refers to the percentage of grain that passes over a certain sizescreen, usually “size 13.” Seedling vigor refers to the early growth ofa seedling and is often times measured via a visual ratings system, from1-9, with higher scores indicate more seedling vigor. Yield is measuredas quintals per hectare, while test weight of seed is measured askilograms per hectoliter.

Typical performance data for canola includes, without limitation, yield,oil content, beginning bloom date, maturity date, plant height, lodging,seed shatter, green seed percentage, winter survival, and diseaseresistance. Yield refers to pounds per acre at 8.5% moisture. Oilcontent is a determination of the typical percentage by weight oilpresent in the mature whole dried seeds. Beginning bloom date refers tothe date at which at least one flower is on the plant. If a flower isshowing on half the plants, then canola field is in 50% bloom. Maturitydate refers to the number of days observed from planting to maturity,with maturity referring to the plant stage when pods with seed colorchange, occurring from green to brown or black, on the bottom third ofthe pod bearing area of the main stem. Plant height refers to theoverall plant height at the end of flowering. The concept of measuringlodging using a scale of 1 (weak) to 9 (strong) is as previouslydescribed. Seed shatter refers to a resistance to silique shattering atcanola seed maturity and is expressed on a scale of 1 (poor) to 9(excellent). Winter survival refers to the ability to withstand wintertemperatures at a typical growing area. Winter survival is evaluated andis expressed on a scale of 1 to 5, with 1 being poor and 5 beingexcellent. Disease resistance is evaluated and expressed on a scale of 0to 5 where: 0=highly resistant, 5=highly susceptible. The WesternCanadian Canola/Rapeseed Recommending Committee (WCC/RRC) blacklegclassification is based on percent severity index described as follows:0-30%=Resistant, 30%-50%=Moderately Resistant, 50%-70%=ModeratelySusceptible, 70%-90%=Susceptible, and >90%=Highly susceptible.

Typical performance data for cotton includes, without limitation, yield,turnout, micronaire, length, fiber strength of cotton and color grade.Yield is measured as pounds per acre. Turnout refers to lint and seedturnout which is calculated as the percentage of lint and seed on aweight basis as a result of ginning the sub sample from each treatment.Micronaire refers to fiber fineness and maturity and are measured usingair flow instrument tests in terms of micronaire readings in accordancewith established procedures. Fiber length is reported in 1/32 of an inchor decimal equivalents. Fiber strength is measured in grams per tex andrepresents the force in grams to break a bundle of fibers one tex unitin size. Color grade for cotton takes into consideration the color,fiber color and whiteness of cotton leaves. Color grade may bedetermined using a two digit scale. The two digit number is anindication of the fiber color and whiteness (i.e. 13, 51, or 84). Thefirst digit can range from 1 to 8 representing overall color with 1being the best color and 8 representing below grade colors. The seconddigit represent a fiber whiteness score. This number ranges from 1 to 5,with 1 representing good white color and 5 representing yellow stained.The second number in the overall color grade represents the leaf scoreand represents leaf content in the sample.

Typical performance data for rice includes, without limitation, yield,kernel length, straw strength, 50% Heading, plant height, and totalmilling, and total milling. Yield is measured as bushels per acre at 12%moisture. Straw Strength refers to lodging resistance at maturity and ismeasured using a numerical rating from 1 to 9 where 1=Strong (nolodging); 3=Moderately strong (most plants leaning but no lodging);5=Intermediate (most plants moderately lodged); 7=Weak (most plantsnearly flat); and 9=Very weak (all plants flat). 50% heading refers tothe number of days from emergence until 50% of the panicles are visiblyemerged from the boot. Plant height is the average distance from thesoil surface to the tip of erect panicle. Total milling refers to thetotal milled rice as a percentage of rough rice. Whole milling refers torice grains of ¾ length or more expressed as a percentage of rough rice.

Of course, other types of performance data may be associated with othertypes of plants, including without limitation, other grains, fruits,vegetables, and flowering plants.

The environmental and physiological landmark data may be historicalusing historical meteorological information along with soils and otheragronomic information or collected using National Oceanic andAtmospheric Association and/or other public or private sources ofweather and soil data. Potential environmental and physiologicallandmark data that may be collected includes but is not limited to wind,drought, temperature, solar radiation, precipitation, soil type, soiltemperature, soil pH, planting and harvesting dates, irrigation, tiledarea, previous crop, fertilizer including nitrogen, phosphorous, andpotassium levels, insecticide, herbicide, and biotic data, for example,insects and disease. The environmental and physiological landmark datamay then be analyzed in light of genotype performance data to determineG×E interactions.

Models

Several models for determining G×E interactions exist. Base models groupor classify the locations used to test the hybrids, include severalvariance components, and stratify the hybrids, for example, according tolocations among station-year combinations, locations, or other chosenvariances. Of course instead of using stations, the locations can beassociated with strip trials, on-farm comparisons, or based oninformation acquired from producers or others.

For example, as shown in Table 1, one base model Year Station (YS)groups the locations by year-stations where a year-station designates aunique site or location by year. Other variances include blocks withinlocations within year-stations, hybrids, hybrids by year-station dividedby the sum of hybrids by locations within year station locations as wellas a residual. The YS model is disadvantageous in that a givenlocation's environment will vary over time so that the G×E informationgleaned from the model may not be relevant for predicting hybrids thatwill perform well in the same location next year.

Another model for determining G×E interactions disclosed in Table 1,groups different sites by location. Other variances for the G×E modelinclude blocks within locations, hybrids, hybrids by locations, as wellas a residual. However, the G×E model is disadvantageous in that agenotype grown in locations with differing environmental conditions mayhave similar performance results, complicating the analysis of thespecific environmental conditions that play a role in contributing togenotype performance and reducing the certainty of predicting productperformance.

Unlike the previous models mentioned, the present inventors contemplatedetermining G×E interactions using a model referred to herein asEnvironmental Classification that groups locations by environmentalclassifications. Thus, variances for this model include locations withinenvironmental classifications, blocks within locations withinenvironmental classifications, hybrids, hybrids by environmentalclassifications divided by hybrids by locations within environmentalclassifications and a residual. TABLE 1 Models for determining G × Einteractions Environmental Model Year-Station G × E ClassificationVariance for Location within Location Location within locationyear-station environmental classification Variance for blocks withinblocks within blocks within location locations within locationslocations within year-station environmental classifications Variance forhybrids hybrids hybrids hybrids Stratifications hybrid by year- hybridby hybrid by station/hybrids by locations environmental locations withinclassifications/ locations hybrid by locations within environmentalclassifications

Burdon has shown that genetic correlation between G×E interactions canbe estimated. (Burdon, R. D. 1977. Silvae Genet., 26: 168-175.). G×Eanalysis may be performed in numerous ways. G×E interactions may beanalyzed qualitatively, e.g. phenotype plasticity, or quantitativelyusing, for example, an analysis of variance approach. (Schlichting, C.D. 1986. Annual Review of Ecology and Systematics 17: 667-693.).Statistical analysis of whether a G×E interaction is significant andwhether environmental changes influence certain traits, such as yieldperformance, of the genotypes evaluated may be performed using anynumber of statistical methods including but not limited to, rankcorrelation, analysis of variances, and stability.

Rank Correlation

The most basic categorization of G×E interaction is to evaluate G×Einteractions by performing a rank correlation according to standardizedtests, for example, Spearman. The Spearman rank correlation may beperformed to examine the relationships among genotypes in differentenvironments, for example, crossover interactions that occur when twogenotypes change in rank order of performance when evaluated indifferent environments. FIG. 2 illustrates an example of G×Einteractions and cross-over interactions (COI) between two differentvarieties, Var A and Var B, in four different environmental classes, Env1, Env 2, Env 3 and Env 4. FIG. 2A shows that Var A and Var Bout-perform each other in different environments indicating theoccurrence of both G×E and COI. FIG. 2B shows that Var A performedbetter than Var B in each environment, indicating G×E interactions butno COI. In contrast, FIG. 2C shows that Var A and Var B each performedconsistently with respect to each other in all four environments,indicating lack of G×E interactions.

Analysis of Variance (ANOVA)

Alternately, G×E interactions may be analyzed using an analysis ofvariance method (ANOVA) (Steel, R. G. D and J. H. Torrie. 1980.Principles and Procedures of Statistics, 2nd edition) over environmentsto determine the significance of genotypes, environments and G×Einteractions. G×E interactions may also be analyzed using ASREML(Gilmour, A. R., Cullis, B. R., Welham, S. J. and Thompson, R. 2002ASReml Reference Manual 2nd edition, Release 1.0 NSW AgricultureBiometrical Bulletin 3, NSW Agriculture, Locked Bag, Orange, NSW 2800,Australia.) for the computation of variance components, and thegeneration of GGE biplots (Cooper, M., and I. H. DeLacy. 1994. Theor.Appl. Genet. 88:561-572; Yan, W. and M. S. Kang. 2003. GGE BiplotAnalysis: A Graphical Tool for Breeders Geneticists, and Agronomists.CRC Press. Boca Raton, Fla.). FIG. 3 and FIG. 4 illustrateenvironment-standardized GGE biplot of grain yield of 18 maize hybrids(H1-H18) grown in 266 environments over three years, stratified by stateor by environmental class respectively.

Stability

Once certain genotypes are identified that perform well in a targetenvironment they may be analyzed to determine which hybrids are morestable in yield or other metrics using various methods. One method usesa regression of genotypic performance on an environmental index. Ingeneral, the environmental index is the deviation of the mean phenotypeat environment from the overall mean phenotype of all environments.Thus, the phenotype of an individual genotype with each environment isregressed on the environmental index, as described in Bernardo R. 2002.Quantitative Traits in Plants. Stemma Press, Woodbury, Minn. to generatea slope (b-value) for each genotype/cultivar evaluated. Other methodsinclude the joint regression analysis method proposed by Perkins, J. M.and Jinks, J. L. 1968. Heredity. 23: 339-359, Finlay, K. W. andWilkinson, G. N. 1963. Aust. J. Res. 14: 742-754 and Eberhart, S. A. andRussell, W. A. 1966. Crop Sci. 6:36-40 to calculate the regressioncoefficient (b), S. E. and variance due to deviation from regression(S2d) as a parameter of stability and adaptability. The model describedby Eberhart and Russell has the following formula:P _(ij) =μ+g _(i) +b _(i) t _(j)+δ_(ij) +e _(ij)

where

-   -   P_(ij) is the mean phenotype of genotype or cultivar i in        location j,    -   μ is the grand mean across the whole experiment for all        genotypes and locations,    -   g_(i) is the effect of genotype i across all locations    -   b_(i) is the linear regression of P_(ij) on t_(j),    -   t_(j) is the environmental index, that is the effect of        environment j across all genotypes),    -   δ_(ij) is the deviation P_(ij) from the linear regression value        for a given t_(j) and    -   e_(ij) is the within environment error.        Categorization of Land Bases into Environmental Classes

Using the information collected for or from G×E analysis, the land basesmay be categorized into environmental classifications. FIG. 5illustrates one possible schematic for categorizing different land basesinto environmental classifications. With reference to FIG. 5, one methodof categorizing environmental classifications is illustrated as a flowchart which provides environmental classifications based on temperatureand/or high photo/sunlight. If all maximum temperatures are greater than28° Celsius 42, then the land base may be categorized as eitherTemperate Dry 54, Temperate Humid 52, Temperate 56, or Subtropical 48.If all maximum temperatures are greater to or equal to 30° Celsius andsolar radiation is greater than 24 and 21 at a given crop developmentstage, e.g. v7-R1, R3-R6 40, then the land base is characterized asTemperate Dry 54. If the maximum temperature is not greater than orequal to 30° Celsius and solar radiation is not greater than 24 at agiven crop development stage, e.g. V7-R1 and 21 for R3-R6 respectively40, then the land base is characterized as Temperate 56. However, if themaximum temperature is less than 30° Celsius and solar radiation isgreater than 24 and 21 at a given crop development stage 50, then theland base is characterized as Temperate Humid 52. If the maximumtemperature is not less than 30° Celsius and solar radiation is notgreater than 24 and 21 at a given crop development stage 50, then theland base is characterized as Temperate 56. If all maximum temperatures42 for the land base are less then 28° Celsius than the land base ischaracterized as High Latitude 44. In contrast, if all maximumtemperatures 42 for the land base are not less then 28° Celsius and theland base has a photoperiod less than 13.4 hours/day 46, then the landbase is Subtropical 48.

Categorizing land bases into environmental classifications has severaladvantages. First, environmental classifications can bring anunderstanding of the various environments under which crops areproduced. Second, occurrence probabilities for each environmentalcategory can be assigned to each geographic location and the frequencyof the classifications determined using routine methods. FIG. 6 is a bargraph representation of the frequency of various environmental classesamong TPEs or METs. The frequency for each environmental class, e.g.temperate, temperate dry, temperte humid, high latitude, andsubtropical, is given as a percent of the total TPE or MET tested ingiven year or across years. FIG. 7 illustrates potential categories ofenvironmental classes identified throughout the United States in 1988and their locations; these include temperate, temperate dry, temperatehumid, high latitude, and subtropical classes. It will be apparent toone skilled in the art that other environmental classifications mayadded as identified or deemed relevant to G×E interactions for variouscrops.

Some of the environmental classification may be defined using generalcharacteristics of climates. For example, temperate may be used to referto regions in which the climate undergoes seasonal change in temperatureand moisture; typically these regions lie between the Tropic ofCapricorn and Antarctic circle in the Southern Hemisphere and betweenthe Tropic of Capricorn and the Arctic circle in the NorthernHemisphere. Temperate humid may refer to regions in which the climateundergoes seasonal change in temperature and moisture and has morehumidity than a temperate environment. High latitude as an environmentalclass may refer to regions that have a longer photoperiod than and istypically north of a particular latitude. A subtropical class may referto regions enjoying four distinct seasons usually with hot tropicalsummers and non-tropical winters with a shorter photoperiod/day length;typically these regions lie between the ranges 23.5-40° N. and 23.5-40°S. latitude. The environmental classes may also be defined by bioticfactors, such as diseases, insects, and/or characteristic of a plant.For example, an ECB class may refer to regions having European CornBorers (ECB) or the suspected presence of ECB as evidenced bypreflowering leaf feeding, tunneling in the plant's stalk, postflowering degree of stalk breakage and/or other evidence of feeding. Theenvironmental class Brittle may be used to refers to regions where stalkbreakage of corn occurs or is apt to occur near the time of pollinationand is indicative of whether a hybrid or inbred would snap or break nearthe time of flowering under severe winds.

It is to be understood that the environmental classifications may beused and defined differently for different crops/genotypes and thatthese definitions may vary from year to year, even for the same crops orgenotypes. For example, in 2000-2003, trials conducted studying G×Einteractions among Comparative Relative Maturity (CRM) hybrids of CRM103-113 in different environments identified seven differentenvironmental classes—temperate, temperate dry, temperate humid, highlatitude, subtropical, ECB, and brittle. For the study purposes,temperate was identified/defined as having a low level of abioticstresses, a growing season adequate for CRM 103-113, and found to befrequent in Iowa and Illinois. Temperate dry was defined as temperatewith high sunlight interception/intensity found to be frequent inNebraska, Kansas, and South Dakota. Temperate Humid was defined assimilar to the temperate environmental class but had a complex of bioticfactors, such as leaf disease, that may differentially affect productperformance. Temperate humid was also characterized as having atemperature and solar radiation lower than that identified in thetemperate environmental class and found to be frequent in Indiana, Ohio,and Pennsylvania. The High Latitude environmental class was found togrow corn CRM 103 and earlier (growing hybrids) and experienced coldertemperatures than the Temperate environmental class but with longerday-length. This environmental class was found to be frequent in Canada,North Dakota, Minnesota, Michigan, and Wisconsin. The fifthenvironmental class, Subtropical, was characterized as warm and humidwith a short day-length and found frequently in the Deep South of theUnited States. Another environmental class identified was European CornBorers (ECB) and defined as having Bacillus thuringiensis (Bt) hybridsthat outyielded base genetics by at least 10%. The last environmentalclass Brittle defined areas with significant brittle damage withdifferential effect on products.

Once areas of land are categorized as environmental classes, these areasmay be used in METs. Ultimately, the observed genotype performances inMETs can be linked by the environmental class to the TPE. By evaluatingproduct performance in a target environment, rather than merelyperformance differences in METs, genotype performance data from multipletest environments can be correlated to a target environment and used topredict product performance. This correlation between a genotype'sperformance and the target environment or environmental classificationwill lead to more precise product placement since the genotypeperformance is characterized within an environmental class in which itis adapted and most likely to experience after commercialization,consequently resulting in improved and more predictable productperformance. The analysis of G×E interactions facilitates the selectionand adoption of genotypes that have positive interactions with itslocation and its prevailing environmental conditions (exploitation ofareas of specific adaption). G×E analysis also aids in theidentification of genotypes with low frequency of poor yield or otherperformance issues in certain environments. Therefore, G×E analysis willhelp in understanding the type and size of G×E interactions expected ina given region. The present inventors contemplate that proper selectionof hybrids for a particular land base will improve agriculturalpotential of certain geographic areas by maximizing the occurrence ofcrop performance through the use of the environmental classification. Inaddition, this approach allows the use of statistical and probabilitybased analysis to quantify the risk of product success/failure accordingto the frequency of environment classes and the relative performance ofgenotypes within each environment class. This early identification andselection of hybrids would enable seed producers to start seedproduction and accelerate the development of hybrids in winter nurseriesin warmer southern climates.

Moreover, environmental classification allows for the creation of anenvironmental profile for all or any part of the land base classified.Environmental classifications can be determined for each producer's landbase. Similarly, the environmental performance profile ofcultivars/hybrids can be determined through field experimentation orpredicted using G×E analysis. In combining environmental classificationfrequencies for a particular land base and product performance byenvironmental classification, performance measurements are given theappropriate amount of relevance or weight for the land base in question.For example, the data are weighted based on long-term frequencies tocompute a prediction of hybrid performance.

Use of G×E in Producer's Selection

According to another aspect of the present invention, a method of usinginformation that documents the environmental profile over time of a cropproducer's land base, the environmental performance profile of cropcultivars, and the producer's objectives to select a portfolio ofcultivars that maximizes and quantifies the probability that theproducer's objectives for productivity will be met. Environmentalclassification can be used to assist in this process.

Environmental classification can be used to determine the primaryenvironmental drivers of G×E interaction in crops such as corn. That is,what are the primary environmental factors that cause change in therelative performance of hybrids. With this knowledge, crop productionareas can be categorized into environmental frequency classes. Withinthese classes, hybrids tend to perform (as measured by yield, quality,or other performance data) relatively similar to one another. Acrossthese classes, the relative performance of hybrids tends to besignificantly different. Using historical meteorological informationalong with soils, pests, and other agronomic information, the frequencyof these environments can be determined. This allows the creation of anenvironmental profile for all or any part of the geography classified.That is, a frequency distribution of the occurrence of the keyEnvironment Classes. This can be done for each crop producer's landbase.

Similarly, the environmental performance profile of crop cultivars canbe determined through field experimentation. That is, a description ofrelative performance of cultivars can be determined in each of the keyenvironment classes. In combining Environment class frequencies for aparticular land area and product performance by Environment Class,performance measurements are given an appropriate amount of relevance orweight for the land area in question

Thus, this aspect of the invention involves combining of thisinformation at the producer's level to optimize crop productivity insuch a way that it maximizes the probability of the producer's businessoperation reaching its productivity goals. The present inventioncontemplates that information can be used from any number ofclassification schemes to the selection of cultivars with the objectiveof maximizing the probability of attainment of the productivity andbusiness goals of a crop producer's operation.

The approach of this aspect of the present invention does so by usingcompiled long term geo-referenced weather, soils, and agronomic dataincluding biotic factors for the producer's land base to categorize theland base in terms of how frequently annual environmental variationoccurs to a degree that is likely to impact relative hybrid performance.In addition, it can incorporate the producer's business objectivesincluding, but not limited to preparedness to take risk. The presentinvention is able to combine environmental variability with producerbusiness information to create a producer profile. Product performanceinformation stratified by the same criteria is used to define theproducer's environmental profile (for example, environmental classes)which is then integrated with the producer's profile.

The relative hybrid performance information that is relevant to theproducer's land base is used regardless of when and where it wasgenerated. The present inventors are first to predict future performanceof genotypes and quantify probability/risk associated with thatperformance using data from environments that are considered to besubstantially equivalent in terms of relative hybrid response. Theresult is a more robust and predictive data set thus allowing moreinformed product selection decisions that, over time will result in ahigher probability of a producer operation meeting business objectivesfor productivity.

FIG. 8 illustrates information flow according to one embodiment of thepresent invention. In FIG. 8 there is an environmental profile 100. Theenvironmental profile can be based on one or more inputs such asenvironment classes 102, meteorological information 104, agronomicinformation 106, or field experimentation 108. In FIG. 1 there is also aproducer profile 110. The producer profile 110 is based on one or moreinputs such as risk tolerance 112 of the producer, business goals 114 ofthe producer, productivity goals 116, financing 118 considerations,third party needs 119, for example a landlord, or insurance/riskmanagement and marketing 120 considerations. The environmental profile100 and the producer profile 110 are combined in order to producerecommendations 122. The recommendations 122 can include risk managementtools, a recommended seed product, a recommended mix of seed products,production practice recommendations, such as chemical applicationinformation, or any number of other specific recommendations as may beappropriate based on the particular environmental profile 100 andproducer profile 110.

FIG. 9 illustrates one embodiment of a system 124 for producing productrecommendations. In FIG. 9, a processor 126 accesses informationassociated with a producer profile 110, an environmental profile 100,and a genotype by environment database 132. There is an input device128, a recommendation output 129, a records output 131, and a display130 operatively connected to the processor. The records output 131 maybe to maintain a record for the producer. The present inventioncontemplates that the processor 126 can be associated with a computersuch as handheld computer as may be convenient for a dealer or salesagent. The present invention also contemplates that the producer profile110, environmental profile 100, and genotype by environment database 132may be accessible over a network, including a wide-area network such asthe Internet.

Using the information in the producer profile 110, environmental profile100, and genotype-by-environment database 132, the processor applies oneor more of a product selection algorithm module 134, a productcomparator 136, a production practice module and a risk comparator 138,and a product portfolio module 140. These and/or other modules arecollectively the recommendation logic 142. In a simple case, the productselection algorithm module 134 would take information from theenvironmental profile 100, such as an environmental classification(“Temperate”, for example) in addition to information from the producerprofile 110, such as a producer objective (“Maximize Yield”, “RiskMinimization”, “Low Harvest Moisture” for example) and match thesecriteria to products in the genotype-by-environment database 132. Ofcourse, more specific criteria could be examined as would be the casewith more complex environmental profile information and more complexproducer profile information.

FIG. 10 illustrates one embodiment of a screen display 144 of a softwareapplication the present invention. In FIG. 10, a user is given thechoice of selecting “DEFINE ENVIRONMENTAL PROFILE” 146, “DEFINE PRODUCERPROFILE” 148, and “VIEW RECOMMENDATIONS” 150. Of course, the presentinvention contemplates that software and its accompanying user interfacecan be implemented in any number of ways.

FIG. 11 illustrates one embodiment of a screen display 152 of a softwareapplication of the present invention. In FIG. 11, a recommendation isgiven which includes a plurality of products 154, an associated numberof acres 156 associated with each of the products, a risk/probabilityassessment 157, and a recommended crop revenue assurance 158. Thepresent invention provides for decreasing the amount of risk associatedwith selection of a particular seed product by instead selectingmultiple products with different G×E interactions in order to reducerisk associated with environmental variations. The resulting selection,is somewhat akin to selection of stocks in a stock portfolio.

FIG. 12 and FIG. 13 illustrate embodiments of user interfaces to use inprecision farming applications. In FIG. 12, the user interface 170includes site-specific information associated with location information172. The present invention contemplates that other site-specificinformation or historical information is accessible based on thelocation information 172 and may be used in product selections. Inaddition, environment and production information is collected. Examplesof such information includes maturity days 176, input traits 178, outputtraits 180, seed treatment 182, tillage practices 174 used, the plantingpopulation 184, nitrogen utilization 186, and drought impact based onenvironmental classification drought frequency information 187 and soiltype. In addition, field attribute information 185, such as, but notlimited to crop history, soils, or other information, may be used. Inaddition, other types of production records associated with a particularlocation may be used. Based on this information and informationassociated with the location 172, a recommendation 188 of at least onehybrid seed product is made. Where multiple recommendations are made,the recommendations can be ranked as well as a risk assessment 189 suchas shown.

FIG. 13 illustrates another embodiment of a user interface 200 that canbe used in crop production applications. Site specific information iscollected such as location 172, soil type 174, and number of acres 202.In addition, there is the option to import precision farming data 204 aswell as import environment of frequency data 205. There are also theoptions to set production practices, set environmental assumptions, setrisk levels, and set the maximum number of hybrids 212. Based on theinputs, a portfolio is created that includes a plurality of products214, an associated number of acres 216 to plant for each product, arecommendation 217 of at least one hybrid seed product, a riskassessment 218, and revenue or crop insurance 219. Where multiplerecommendations are made, the recommendations can be ranked. There isalso an option to generate precision farming information 220 based onthis information, such as a prescription map. The present inventioncontemplates that the precision farming information may indicate whichacres to plant with which hybrids, give specific production practiceapplication (such as chemical application rates), or otherrecommendations.

FIG. 14 illustrates one example of a field-by-field analysis showingproduct recommendations for a land base of a producer. As shown in FIG.14, different land areas within a producer's land base have differenthybrids associated with them. The present invention contemplatesproducing such a map or field-by-field recommendations where multipleproducts are recommended. It should further be understood that a singleproducer or other user may have operations in a number of geographicallydiverse locations, and not necessarily the nearby fields illustrated inFIG. 14.

It should also be appreciated that the use of environmentalclassification and G×E interactions should be effectively communicatedto customers. The effectiveness of the environmental classificationprocess is based in part on its ability to use historical data from manylocations so that all available data is used. This aspect ofenvironmental classification would seem counter-intuitive to a customerwho primarily relies upon personal knowledge in the local area. Thecustomer's confidence in firsthand production knowledge can be used toassist in increasing confidence in environmental classification.

FIG. 15 illustrates one example of the methodology of this aspect of theinvention to assist in explaining these concepts to a producer. In step300 site-specific data collection for a land base is performed. Based onthis site-specific data collection, in step 302, the land base is givenan environmental classification. In addition to this information, thetype of hybrid selected in the previous year and its performance isprovided by the producer in step 304. In step 306, a prediction is madeas to the previous year's production based on environmentalclassification. In step 308, the predicted results are compared with theactual results. The present invention also contemplates not requiringperformance results from the producer until after the previous year'sresults have been predicted in case the producer is not confident thatan independent prediction is made.

FIG. 16 illustrates one example of a screen display showing suchcomparisons. In FIG. 16, performance predictions (yield) are made for anumber of different hybrids for both the previous year and the currentyear. In addition, a risk assessment for each hybrid may also beprovided. The producer can compare the prediction for the previous yearwith the actual performance for that year in order to understand howwell the environmental classification method can predict a result. Ifthe producer is confident in the method's ability to correctly predict aresult, the producer will be more inclined to use the prediction madefor the coming year. The present invention contemplates that the same orsimilar information can be presented in any number of ways. It shouldfurther be understood that such a demonstration assists in illustratingthe accuracy of the system in predicting relative performancedifferences between seed products. Due to the number of potentialvariables and difficulty in controlling such variables, accurateprediction of absolute performance is generally not a reasonable goal.However by selecting appropriate environmental classifications, usefulinsight into relative performance can be provided.

The present invention further recognizes the value of land base specificcrop production records which include inputs, outputs, and parametersassociated with environmental classification. The present inventionprovides a method to use such records as a part of the environmentalclassification system to improve analysis and recommendations. Thisincludes, but is not limited to, risk assessment, input recommendations,recommendations for production practices.

Financial Incentives for Use of G×E and/or Environmental Classification

The present invention recognizes that agricultural input suppliersbenefit from the success which they assist crop producers in obtaining.For example, when a seed product performs exceptionally well for aproducer, such a seed product may be perceived as being of higherquality than competing products in future years. When a seed productperforms poorly, such as seed product may be perceived as being of alower quality or undesirable and the producer and other producers may bedisinclined to purchase the seed product in future years. The samesituation may apply for other types of inputs, including, but notlimited to pesticides and fertilizers. It should be appreciated thatthese perceptions are not facts, but merely one data point. While thegenotype for each of the products may be capable of producing highperformers, the circumstances regarding the environment, and theresulting G×E interactions may have limited performance. Therefore, theresult of the performance has very limited utility when viewed inisolation because the same or highly similar environmental conditionsmay not be present in the future years. The use of the environmentalclassification system of the present invention is advantageous as itincorporates significant data and therefore does not limit one to anisolated and restrictive view of the performance of an agriculturalinput.

As previously indicated, there may be some resistance to use of anenvironmental classification system by particular producers because itrequires reliance on data that was not observed firsthand. Also, aspreviously indicated there is a benefit to suppliers of agriculturalinputs to have producers provide the best results. To increase thelikelihood of those results the present invention provides for promotingthe use of environmental classification or other systems that take intoaccount G×E interactions by providing a financial incentive to producersfor doing so. The financial incentive can take on one or more of manydifferent types. This can include a rebate on purchase price, financingfor purchases at lower than normal rates such as prime or prime minus 1percent financing. According to this methodology, a recommendation for aproducer would be made using an environmental classification system. Ifthe producer accepted the recommendation and made purchases based on therecommendation then the producer would receive the additional financialincentive. The recommendation may include the selection of one or morespecific products, or may include a recommendation that one or moreproducts be selected from a particular set of products. Such amethodology encourages the producer in making decisions based on G×Einteractions and/or environmental classification.

Because environmental classification provides for managing risk, thepresent invention provides for others, instead of, and/or in addition toproducers and input suppliers to benefit from this risk management.Generally, others with an interest in production management decisionsinclude other stakeholders. Stakeholders can include banks or otherfinancial institutions. Stakeholders could also include landlords,purchasers of resulting crops, or others. In one embodiment of thepresent invention, a bank or other financial institution requires orencourages a producer to use environmental classification for productselection and/or product positioning. For the previously indicatedreasons, a producer may be reluctant to use environmental classificationto manage risk. However, a bank or other financial institution providingfinancing desires to minimize risk. As a condition of financing, thebank or other financial institution may require the use of environmentalclassification.

In addition, a bank or other financial institution may use environmentalclassification for evaluating a producer's current or past selection ofagricultural inputs. This is one manner in which a bank or financialinstitution may evaluate risk. Where a producer regularly makes poorselections of agricultural inputs, there may be greater risk associatedwith providing lending. Where such risks exist, a financial institutionmay decide to not lend money, or loan money under terms which betteroffset increased lending risks associated with the producer, such ashigher interest rates. Where a producer has historically made poordecisions regarding agricultural inputs, a financial institution mayalso have additional incentive to require the producer to use therecommendations provided by an environmental classification system.Thus, the use of environmental classification also provides a method forevaluating past decisions of a producer in relationship to currentdecisions.

The methodology of the present invention can be applied to assisting inmanaging the risk associated with a loan transaction involving aproducer and a lender. FIG. 18 provides an example of such arelationship. In FIG. 18, there is an agricultural producer 500 and alender 504 with a relationship defined by lending terms and conditions502. The present invention provides for using environmentalclassification, product recommendations, and production practicerecommendations in determining the lending terms and conditions. Thelending terms and conditions may include principal amounts, interestrates, and repayment terms. In addition the lending terms and conditionsmay have specific terms and conditions relating to environmentalclassification analysis, product recommendations based on environmentalclassification analysis, or production practice recommendations 506based on environmental classification analysis. The use of descriptionsof genotype-by-environment interactions, including environmentalclassification in association with risk assessments and a portfolioapproach, enhances the ability of the lender to manage risk. Therefore,the lender may provide benefits or incentives to the agriculturalproducer who, for example, agrees to plant only those hybrids or otheragricultural inputs appropriate for the environmental classification ofthe producer's land base. The benefit or incentive may be, withoutlimitation, a reduced interest rate, a greater principal amount, or morefavorable repayment terms. The lender may also require the use ofapproved hybrids appropriate for the environmental classification of theproducer's land base. The lender might also require the use of riskmanagement instruments, such as crop insurance or crop revenue insurancebased on environmental classification of the land base and therecommendations and risk assessments 508 for seed products, herbicides,insecticides, and other inputs or production practices. Of course, thepresent invention contemplates combining this information with otherinformation that may be used in determining whether or not to provide aloan and determining the lending terms and conditions. For example,production practice or production history information 503 may also beused. The present invention recognizes that genotype-by-environmentinteraction risks can be described and managed and that managing thisrisk, particularly at a producer level, allows for better managing offinancial risks associated with crop production for all stakeholders.

Crop Insurance

The environmental classification methodologies of the present inventionprovide a statistically significant means to manage risk associated withgenotype-by-environment interactions. The present invention provides fora number of methods and tools to assist in the management of risks and anumber of products based on the increased understanding of risk and thepredictive capabilities of these environmental classificationmethodologies.

One such aspect of the present invention relates to selection of a cropinsurance plan. Although there are various software tools or othermechanisms available for selecting a crop insurance plan, the selectionof a proper crop insurance plan is based on different scenarios of cropperformance. One example of such a software tool is disclosed in U.S.Patent Publication No. 2005/0027572A1, herein incorporated by referencein its entirety. The present invention provides a means for determiningmore appropriate scenarios of crop performance which can then in turn beused to select an appropriate crop insurance plan. For example,environmental classification can be used to select preferred seedproducts as previously explained. The proper selection of seed productsusing environmental classification results in a statistical likelihoodof better performance in a properly classified land base in a givenyear.

Although the present invention is not limited any specific types of cropinsurance, specific examples of crop insurances are described herein.Examples of crop insurance include catastrophic coverage (CAT), CropRevenue Coverage (CRC), Multi-Peril Crop Insurance (MPCI), and RevenueAssurance (RA).

In the United States, Catastrophic Coverage or CAT is the minimum levelof MPCI coverage provided by FCIC. CAT insurance was created by Congressin 1994 to replace ad hoc disaster assistance—providing coverage for theequivalent of 27.5% of the value of the crop. Purchasing this minimumlevel of coverage allows producers to qualify for emergency disasterbenefits and other farm support programs administered by local FarmService Agencies. Farmers pay no premium, only a small administrationfee per crop per county regardless of the type of crop or the number ofacres. The policy reimburses lost bushels below the 50% yield guaranteeat 55% of the established price.

Crop Revenue Coverage (CRC) provides coverage against the same perils asMPCI with the addition of upward and downward commodity market pricemovement. CRC protects against lost revenue caused by low prices, lowyields or any combination of the two. The policy sets a market-basedrevenue guarantee in the spring before planting which is compared tocalculated revenue raised using harvest price averages.

CRC insurance typically places a floor under yield and price risk,guaranteeing the policyholder will have inventory available or have itreplaced at cash value. This allows producers to utilize variouscommodity marketing tools on guaranteed bushels at little to no risk.When harvest markets increase, so does the policy liability but at noadditional premium charge.

The present invention provides for incorporating environmentalclassification information into the policy formation process. Inparticular, predicted yields based on environmental classification areused to set the market-based revenue guarantee in the spring. Thepresent invention contemplates providing incentives to crop producers touse environmental classification. One example of such an incentive is toprovide an increased revenue guarantee when the selection of seedproducts or other inputs or production management techniques areselected using environmental classification methodology. Another exampleof an incentive is to reduce premiums when product selections or otherproduction management decisions are made according to recommendationsbased on environmental classification. Reducing the premium of a cropinsurance policy is another example of providing a financial incentiveto a producer for using environmental classification.

Income Protection (IP) is a revenue product that protects againstreductions in gross income when yields or prices fall. In IncomeProtection insurance a revenue guarantee is set prior to planting anddoes not move. Indemnities are paid when actual revenue raised fallsbelow the revenue guarantee. If fall market prices increase, revenueguarantee does not move and indemnities are less likely. The presentinvention provides for incorporating environmental classificationmethodologies with income protection insurance. The revenue guaranteemay be set at least partially based on whether or not the insured usesenvironmental classification methodologies, or a particular product orservice which uses environmental classification in making cropproduction decisions such as type of seed product to use, mix of seedproduct to use, chemical usage, or other crop production decisions.Alternatively, there may be the incentive for lowered premiums where aproducer incorporates environmental classification methodologies intotheir crop production decisions. These are additional examples of wherefinancial incentives are provided to a producer for using environmentalclassification.

Multi-Peril Crop Insurance (MPCI) is a U.S. federally regulated andsubsidized yield guarantee program that covers losses due to adverseweather, insects, wildlife, diseases, replanting, prevented planting,poor quality and even earthquakes and volcanic eruption. Qualifyingclaims reimburse lost bushels (below the established bushel per acreguarantee) at an elected price per bushel.

Bushel guarantees are determined from a straight average of a minimum offour building to a maximum of ten years of actual production history.Approved yield histories permanently attach to the legal descriptionsand the social security number of those with ownership of the crop.

Coverage rates, factors and reporting deadlines are written on a countybasis. Coverage can be tailored by choosing options such as level,price, unit structure and prevented planting benefits. In this type ofpolicy, the present invention also provides for tying incentives to theuse of environmental classification to understand and/or predictenvironment by genetics interactions.

Revenue Assurance (RA) provides coverage against the same perils as MPCIwith the addition of downward price movement and the option to purchaseadditional protection for upward price movement. RA offers coveragelevels of 65% to 85%. For basic and optional units 80% and 85% are onlyon crops and in counties where MPCI allows 80 or 85%. Such a policy usesthe producer's own Actual Production History (APH) to establishguarantees on a unit basis. Prices are established in the same manner asCRC. In this type of policy, the present invention also provides fortying incentives to the use of environmental classification tounderstand and predict genotype-by-environment interactions. Theincentives can include increased coverage levels, decreased premiums, orother incentives.

FIG. 19 illustrates one embodiment of the present invention where cropinsurance is combined with environmental classification to assist inmanaging risk. In FIG. 19 a system 550 for making crop insurancerecommendation is based in part on genotype-by-environment information,such as environmental classification information. In FIG. 19, inputs 574include a producer database 552, a commodity pricing database 554, acounty database 556, an actuarial database 558, a government database560, a genotype-by-environment database 562, and an agronomic/productionpractices database 563. The databases may be accessed locally, or may beaccessible over a network, such as a wide area network, or somecombination thereof. The inputs 574 are operatively connected to aprocessor 564 which is operatively connected to input device 563, arecommendation output 565, a records output 567, and a display 566. Theprocessor is programmed to run one or more crop insurance modules 576,including a crop insurance plan algorithm module 568, a productcomparator module 570, and an options analyzer 572. The presence of thegenotype-by-environment database 562 in the system allows for astatistically more accurate selection of a scenario of crop performance.Although there are various software tools or other mechanisms availablefor selecting a crop insurance plan, the selection of a proper cropinsurance plan is based on different scenarios of crop performance. Oneexample of such a software tool is disclosed in U.S. Patent PublicationNo. 2005/0027572A1, herein incorporated by reference in its entirety.The present invention provides a means for determining more appropriatescenarios of crop performance which can then in turn be used to selectan appropriate crop insurance plan. For example, environmentalclassification can be used to select preferred seed products or otheragricultural inputs as previously explained. The proper selection ofseed products using environmental classification results instatistically greater production in a properly classified land base in agiven year.

The proper use of environmental classification generally reduces therisk of the insurer which can result in increased revenue for theinsurer and the potential for savings for the insured or incentives forthe insured. The present invention also provides for individualunderwriting which is generally considered to result in policies thatare more fair to all parties involved.

The present invention contemplates numerous variations from the specificembodiments provided herein. These include variations in theenvironmental classifications, performance characteristics, software orhardware where used, the type of and other variations.

All publications, patents and patent applications mentioned in thespecification are indicative of the level of those skilled in the art towhich this invention pertains. All such publications, patents and patentapplications are incorporated by reference herein for the purpose citedto the same extent as if each was specifically and individuallyindicated to be incorporated by reference herein.

1. A method for reducing risk associated with making a financialdecision related to crop production, the method comprising: identifyinga land base for the crop production; classifying the land base toprovide an environmental classification; receiving an indication of theseed product selected for production; evaluating relative riskassociated with the production of different genotypes of seed productsby comparing predicted relative performance of a plurality of seedproducts, each seed product having a genotype; wherein the predictedrelative performance being at least partially based on predictedgenotype by environment interactions between each seed product and theenvironmental classification of the land base; wherein the plurality ofseed products includes the seed product selected for production;providing a financial decision at least partially based on the riskassociated with the use the seed product selected for productionrelative to one or more other seed products within the plurality of seedproducts.
 2. The method of claim 1 wherein the financial decision is adetermination of whether to finance the crop production.
 3. The methodof claim 2 wherein the financial decision is a determination of whetherto contract for purchasing crops resulting from the crop production. 4.The method of claim 1 wherein the financial decision a determination ofterms of financing the crop production.
 5. The method of claim 1 whereinthe relative performance comprises relative yield.
 6. The method ofclaim 1 wherein the relative performance comprises relative proteincontent.
 7. The method of claim 1 wherein the relative performancecomprises relative oil content.
 8. The method of claim 1 wherein therelative performance comprises relative starch content.
 9. The method ofclaim 1 wherein the relative performance comprises relative moisturecontent.
 10. The method of claim 1 wherein the financial decision isassociated with terms of an insurance policy.
 11. The method of claim 1wherein the financial decision is associated with terms of a rental orlease agreement for the land base.
 12. A method for providing financing,comprising: evaluating use of agricultural inputs associated with aproducer according to an environmental classification system whereineach of the agricultural inputs being classified according to theenvironmental classification system and a land base associated with theproducer being classified according to the environmental classificationsystem; making a financing decision associated with the producer basedon the step of evaluating.
 13. The method of claim 12 where thefinancing decision is whether to finance the producer.
 14. The method ofclaim 12 wherein the financing decision comprises terms of financing ofthe producer.
 15. The method of claim 12 wherein the wherein the use ofagricultural inputs is a proposed use of agricultural inputs for anupcoming growing season.
 16. The method of claim 12 wherein theenvironmental classification system provides for correlating anenvironmental classification of agricultural inputs with environmentalclassification of a land base associated with the producer to evaluateuse of the agricultural inputs.
 17. The method of claim 12 wherein theagricultural inputs comprise one or more seed products.
 18. A method forproviding a financial incentive for use of an environmentalclassification system in making production management decisions, themethod comprising: providing to a producer recommendations ofagricultural inputs to use at least based on environmentalclassification associated with the agricultural inputs and anenvironmental classification associated with a land base of theproducer; giving the producer a financial incentive select agriculturalinputs based on the recommendations.
 19. The method of claim 18 whereinthe financial incentive comprises a reduced purchase price for one ormore of the agricultural inputs.
 20. The method of claim 18 wherein thefinancial incentive comprises preferred financing terms.
 21. The methodof claim 18 wherein the financial incentive comprises a reduced interestrate on financing.
 22. The method of claim 18 wherein the financialincentive comprises a reduced rate on crop insurance.
 23. A method forproviding a financial incentive for use of genotype by environmentinformation in selecting a seed product, the method comprising:providing to a producer a recommendation of one or more seed products touse to produce crop on a land base associated with the producer, therecommendation based on relative performance of a plurality of seedproducts under environmental conditions associated with the land base ofthe producer and interactions between the genotype of each of theplurality of seed products and the environmental conditions; giving theproducer a financial incentive to accept the recommendation.
 24. Themethod of claim 23 wherein the financial incentive comprises a reducedpurchase price for one or more of the agricultural inputs.
 25. Themethod of claim 23 wherein the financial incentive comprises preferredfinancing terms.
 26. The method of claim 23 wherein the financialincentive comprises a reduced interest rate on financing.
 27. A methodfor providing a crop insurance policy to a producer, the methodcomprising: receiving an evaluation of use of agricultural inputsassociated with a producer according to an environmental classificationsystem wherein each of the agricultural inputs being classifiedaccording to the environmental classification system and a land baseassociated with the producer being classified according to theenvironmental classification system; determining one or more terms ofthe crop insurance policy at least partially based on the step ofevaluating use of agricultural inputs; providing the crop insurancepolicy to the producer.
 28. The method of claim 27 wherein theenvironmental classification system is at least partially based ongenotype by environment interactions.
 29. The method of claim 27 whereinthe crop insurance policy is a crop revenue insurance policy.